Flag to indicate that the algorithm should run the fast/approximative version.
Default: false
Parameter for:
Parameter k for kNN queries.
Default: 30
Parameter for:
Kernel function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction
Known implementations:
Parameter for:
Sample size to use in fast mode.
Parameter for:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
Parameter for:
Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.Algorithm
Known implementations:
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
Algorithms to run
Parameter for:
the file to write the generated data set into, if the file already exists, the generated points will be appended to this file.
Parameter for:
Threshold for minimum frequency as percentage value (alternatively to parameter apriori.minsupp).
Parameter for:
Threshold for minimum support as minimally required number of transactions (alternatively to parameter apriori.minfreq - setting apriori.minsupp is slightly preferable over setting apriori.minfreq in terms of efficiency).
Parameter for:
Scale the data space extension by the given factor.
Default: 1.0
Parameter for:
Half-transparent filling of bubbles.
Default: false
Parameter for:
A gamma-correction.
Default: 1.0
Parameter for:
Additional scaling function for bubbles.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Known implementations:
Parameter for:
Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
Default: false
Parameter for:
The random generator seed.
Parameter for:
Factor for scaling the specified cluster sizes.
Default: 1.0
Parameter for:
The generator specification file.
Parameter for:
Flag to indicate that an adjustment of the applied heuristic for choosing an interval is performed after an interval is selected.
Default: false
Parameter for:
The maximum jitter for distance values.
Parameter for:
The maximum level for splitting the hypercube.
Parameter for:
The minimum dimensionality of the subspaces to be found.
Default: 1
Parameter for:
Threshold for minimum number of points in a cluster.
Parameter for:
Maximum value to allow.
Parameter for:
Minimum value to allow.
Parameter for:
Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
Default: false
Parameter for:
The density threshold for the selectivity of a unit, where the selectivity isthe fraction of total feature vectors contained in this unit.
Parameter for:
The number of intervals (units) in each dimension.
Parameter for:
Class that is used to generate a color histogram.
Class Restriction: implements de.lmu.ifi.dbs.elki.data.images.ComputeColorHistogram
Default: de.lmu.ifi.dbs.elki.data.images.ComputeNaiveRGBColorHistogram
Known implementations:
Parameter for:
Input image for color histograms.
Parameter for:
number of bins
Parameter for:
Class label for the 'positive' class.
Parameter for:
Class to use as scaling function.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.ScalingFunction
Default: de.lmu.ifi.dbs.elki.utilities.scaling.IdentityScaling
Known implementations:
Parameter for:
Clustering algorithm to apply to each partition.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm
Known implementations:
Parameter for:
Database class for each partition. If this parameter is not set, the databases of the partitions have the same class as the original database.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Known implementations:
Parameter for:
Distance to use for the inner algorithms.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.LocalPCAPreprocessorBasedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
Parameter for:
Local PCA Preprocessor to derive partition criterion.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Known implementations:
Parameter for:
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.connection.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.database.connection.FileBasedDatabaseConnection
Known implementations:
Parameter for:
Class label class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.data.ClassLabel
Default: de.lmu.ifi.dbs.elki.data.SimpleClassLabel
Known implementations:
Parameter for:
The index of the label to be used as class label.
Parameter for:
Database class to be provided by the parse method.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.SequentialDatabase
Known implementations:
Parameter for:
The index of the label to be used as an external id. If the external id is an integer value the external id is also used as internal id, otherwise an association with de.lmu.ifi.dbs.elki.database.AssociationID@9aafa626 is set.
Parameter for:
The name of the input file to be parsed.
Parameter for:
Parser to provide the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
Parameter for:
Seed for randomly shuffling the rows for the database. If the parameter is not set, no shuffling will be performed.
Parameter for:
size of the D-neighborhood
Parameter for:
minimum fraction of objects that must be outside the D-neigborhood of an outlier
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Parameter for:
Threshold for minimum number of points within a cluster.
Parameter for:
Threshold for output accuracy fraction digits.
Default: 4
Parameter for:
Flag to use random sample (use knn query around centroid, if flag is not set).
Default: false
Parameter for:
Threshold for the size of the random sample to use. Default value is size of the complete dataset.
Parameter for:
an integer between 1 and the dimensionality of the feature space 1 specifying the dimension to be considered for distance computation.
Parameter for:
The maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector.
Default: 0.0010
Parameter for:
Positive threshold for minumum numbers of points in the epsilon-neighborhood of a point. The value of the preference vector in dimension d_i is set to 1 if the epsilon neighborhood contains more than dish.minpts points and the following condition holds: for all dimensions d_j: |neighbors(d_i) intersection neighbors(d_j)| >= dish.minpts.
Parameter for:
The minimum number of points as a smoothing factor to avoid the single-link-effekt.
Default: 1
Parameter for:
The strategy for determination of the preference vector, available strategies are: [APRIORI| MAX_INTERSECTION](default is MAX_INTERSECTION)
Default: MAX_INTERSECTION
Parameter for:
a comma separated array of integer values, where 1 <= d_i <= the dimensionality of the feature space specifying the dimensions to be considered for distance computation. If this parameter is not set, no dimensions will be considered, i.e. the distance between two objects is always 0.
Parameter for:
The name of the file containing the distance matrix.
Parameter for:
Parser used to load the distance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.DistanceParser
Default: de.lmu.ifi.dbs.elki.parser.NumberDistanceParser
Known implementations:
Parameter for:
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.0010
Parameter for:
Number of bins to use in the histogram. By default, it is only guaranteed to be within 1*n and 2*n of the given number.
Default: 20
Parameter for:
In a first pass, compute the exact minimum and maximum, at the cost of O(2*n*n) instead of O(n*n). The number of resulting bins is guaranteed to be as requested.
Default: false
Parameter for:
Enable sampling of O(n) size to determine the minimum and maximum distances approximately. The resulting number of bins can be larger than the given n.
Default: false
Parameter for:
the band size for Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
Parameter for:
the delta parameter (similarity threshold) for EDR (positive number)
Default: 1.0
Parameter for:
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 0.0
Parameter for:
The number of clusters to find.
Parameter for:
Threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all stroneg eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2.
Default: 0.1
Parameter for:
Threshold for the maximum distance between two approximately linear dependent subspaces of two objects p and q (lambda_q < lambda_p) before considering them as parallel.
Default: 0.1
Parameter for:
the g parameter ERP (positive number)
Default: 0.0
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
The max degree of theFooKernelFunction. Default: 2
Parameter for:
Regularize scores before using Gamma scaling.
Default: false
Parameter for:
Invert the value range to [0:1], with 1 being outliers instead of 0.
Default: false
Parameter for:
The number of reference points to be generated.
Parameter for:
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Parameter for:
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Parameter for:
The number of partitions in each dimension. Points will be placed on the edges of the grid, except for a grid size of 0, where only the mean is generated as reference point.
Default: 1
Parameter for:
The threshold for 'strong' eigenvectors: the 'strong' eigenvectors explain a portion of at least alpha of the total variance.
Default: 0.85
Parameter for:
Threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space.
Default: 0.25
Parameter for:
Optional parameter to specify the number of nearest neighbors considered in the PCA. If this parameter is not set, k is set to the value of parameter mu.
Parameter for:
Specifies the smoothing factor. The mu-nearest neighbor is used to compute the correlation reachability of an object.
Parameter for:
The maximum absolute variance along a coordinate axis.
Default: 0.01
Parameter for:
The number of nearest neighbors considered to determine the preference vector. If this value is not defined, k ist set to three times of the dimensionality of the database objects.
Parameter for:
Use separate frequencies for outliers and non-outliers.
Default: false
Parameter for:
Bins per plane for HSV/HSB histogram. This will result in bpp ** 3 bins.
Parameter for:
The number of nearest neighbors of an object to be considered for computing its INFLO_SCORE.
Parameter for:
The threshold
Default: 1.0
Parameter for:
the kernel function which is used to compute the similarity.Default: class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction
Known implementations:
Parameter for:
The degree of the polynomial kernel function. Default: 2.0
Default: 2.0
Parameter for:
The number of clusters to find.
Parameter for:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Parameter for:
Specifies the distance of the k-distant object to be assessed.
Default: 1
Parameter for:
The average percentage of distances randomly choosen to be provided in the result.
Default: 1.0
Parameter for:
Specifies the k-nearest neighbors to be assigned.
Default: 1
Parameter for:
k nearest neighbor
Parameter for:
k nearest neighbor
Parameter for:
the allowed deviation in x direction for LCSS alignment (positive double value, 0 <= pDelta <= 1)
Default: 0.1
Parameter for:
the allowed deviation in y directionfor LCSS alignment (positive double value, 0 <= pEpsilon <= 1)
Default: 0.05
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LDOF_SCORE.
Parameter for:
Fixed maximum to use in linear scaling.
Parameter for:
Fixed minimum to use in lienar scaling.
Parameter for:
Use the mean as minimum for scaling.
Default: false
Parameter for:
File name of the disk cache to create.
Parameter for:
Distance function to cache.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
The distance function used to select objects for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
The maximum radius of the neighborhood to be considered in the PCA.
Parameter for:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
Parameter for:
Scaling factor for averaging neighborhood
Default: 0.5
Parameter for:
Minimum neighborhood size to be considered.
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LOF_SCORE.
Parameter for:
Distance function to determine the reachability distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Distance function to determine the reference set of an object.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LOOP_SCORE.
Parameter for:
The number of nearest neighbors of an object to be used for the PRD value.
Parameter for:
The number of standard deviations to consider for density computation.
Default: 2.0
Parameter for:
Preprocessor used to materialize the kNN neighborhoods.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.MaterializeKNNPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.MaterializeKNNPreprocessor
Known implementations:
Parameter for:
Distance function to determine the reference set of an object.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
the degree of the L-P-Norm (positive number)
Parameter for:
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be materialized.
Parameter for:
Parser to use as base parser
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
Parameter for:
Metrical index class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.MetricalIndex
Known implementations:
Parameter for:
positive integer specifying the maximal number k of reversek nearest neighbors to be supported.
Parameter for:
Flag to indicate that the approximation is done in the ''normal'' space instead of the log-log space (which is default).
Default: false
Parameter for:
positive integer specifying the order of the polynomial approximation.
Parameter for:
positive integer specifying the maximal number k of reversek nearest neighbors to be supported.
Parameter for:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
Parameter for:
cutoff
Default: 1.0E-7
Parameter for:
expected fraction of outliers
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
A comma separated list of the names of the input files to be parsed.
Parameter for:
Comma separated list of classnames specifying the parsers to provide a database. If this parameter is not set, de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser is used as parser for all input files.
Parameter for:
Normalization class in order to normalize values in the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.normalization.Normalization
Known implementations:
Parameter for:
A comma separated list of normalizations for each representation. If in one representation no normalization is desired, please use the class 'de.lmu.ifi.dbs.elki.normalization.DummyNormalization' in the list.
Parameter for:
a comma separated concatenation of the maximum values in each dimension that are mapped to 1. If no value is specified, the maximum value of the attribute range in this dimension will be taken.
Parameter for:
a comma separated concatenation of the mean values in each dimension that are mapped to 0. If no value is specified, the mean value of the attribute range in this dimension will be taken.
Parameter for:
a comma separated concatenation of the minimum values in each dimension that are mapped to 0. If no value is specified, the minimum value of the attribute range in this dimension will be taken.
Parameter for:
a comma separated concatenation of the standard deviations in each dimension that are scaled to 1. If no value is specified, the standard deviation of the attribute range in this dimension will be taken.
Parameter for:
Revert normalization result to original values - invalid option if no normalization has been performed.
Default: false
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Parameter for:
The factor for reducing the number of current clusters in each iteration.
Default: 0.5
Parameter for:
Directory name (or name of an existing file) to write the obtained results in. If this parameter is omitted, per default the output will sequentially be given to STDOUT.
Parameter for:
Enable gzip compression of output files.
Default: false
Parameter for:
Silently overwrite output files.
Default: false
Parameter for:
Distance function.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
A comma separated list of the indices of labels (may be numeric), counting whitespace separated entries in a line starting with 0. The corresponding entries will be treated as a label.
Parameter for:
A constant big value to reset high eigenvalues.
Default: 1.0
Parameter for:
Class used to compute the covariance matrix.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Parameter for:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.EigenPairFilter
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
Known implementations:
Parameter for:
Flag to mark delta as an absolute value.
Default: false
Parameter for:
The share (0.0 to 1.0) of variance that needs to be explained by the 'strong' eigenvectors.The filter class will choose the number of strong eigenvectors by this share.
Default: 0.85
Parameter for:
A comma separated list of the class names of the filters to be used. The specified filters will be applied sequentially in the given order.
Parameter for:
The threshold for strong Eigenvalues. If not otherwise specified, delta is a relative value w.r.t. the (absolute) highest Eigenvalues and has to be a double between 0 and 1. To mark delta as an absolute value, use the option -pca.filter.absolute.
Default: 0.01
Parameter for:
The number of strong eigenvectors: n eigenvectors with the n highesteigenvalues are marked as strong eigenvectors.
Parameter for:
The share (0.0 to 1.0) of variance that needs to be explained by the 'strong' eigenvectors.The filter class will choose the number of strong eigenvectors by this share.
Default: 0.5
Parameter for:
The sensitivity niveau for weak eigenvectors: An eigenvector which is at less than the given share of the statistical average variance is considered weak.
Default: 1.1
Parameter for:
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.95
Parameter for:
A constant small value to reset low eigenvalues.
Default: 0.0
Parameter for:
Weight function to use in weighted PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions.WeightFunction
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions.ConstantWeight
Known implementations:
Parameter for:
Threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space.
Default: 0.25
Parameter for:
a double between 0 and 1 specifying the threshold for small Eigenvalues (default is delta = 0.01).
Default: 0.01
Parameter for:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
Parameter for:
The Classname of the preprocessor to determine the neighbors of the objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Known implementations:
Parameter for:
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.Preprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.Preprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.PreferenceVectorPreprocessor
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.PreferenceVectorPreprocessor
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
The multiplier for the initial number of medoids.
Default: 10
Parameter for:
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
The intrinsic dimensionality of the clusters to find.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractLocallyWeightedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
Parameter for:
The number of clusters to find.
Parameter for:
The multiplier for the initial number of seeds.
Default: 30
Parameter for:
The dimensionality of the clusters to find.
Parameter for:
a comma separated array of integer values d_i, where 1 <= d_i <= the dimensionality of the feature space specifying the dimensions to be considered for projection. If this parameter is not set, no dimensions will be considered, i.e. the projection is a zero-dimensional feature space
Parameter for:
Number of bins in the distribution histogram
Default: 20
Parameter for:
Use curves instead of the stacked histogram style.
Default: false
Parameter for:
number of selected attributes
Default: 1
Parameter for:
Number of bins to use in the histogram
Default: 20
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
positive integer specifying the maximal number k of reverse k nearest neighbors to be supported.
Parameter for:
The number of nearest neighbors
Parameter for:
The heuristic for finding reference points.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.referencepoints.ReferencePointsHeuristic
Default: de.lmu.ifi.dbs.elki.utilities.referencepoints.GridBasedReferencePoints
Known implementations:
Parameter for:
Result handler class.
Class Restriction: implements de.lmu.ifi.dbs.elki.result.ResultHandler
Default: de.lmu.ifi.dbs.elki.result.ResultWriter
Known implementations:
Parameter for:
Bins per plane for RGB histogram. This will result in bpp ** 3 bins.
Parameter for:
Class label for the 'positive' class.
Parameter for:
The number of samples to draw.
Parameter for:
number of nearest neighbors to consider (at least 1)
Default: 1
Parameter for:
The minimum SNN density.
Parameter for:
Threshold for minimum number of points in the epsilon-SNN-neighborhood of a point.
Parameter for:
the distance function to asses the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter for:
The multiplier for the discriminance value for discerning small from large variances.
Default: 1.1
Parameter for:
The number of shared nearest neighbors to be considered for learning the subspace properties.
Default: 1
Parameter for:
flag to specify bulk load (default is no bulk load)
Default: false
Parameter for:
the strategy for bulk load, available strategies are: [MAX_EXTENSION| ZCURVE](default is ZCURVE)
Default: ZCURVE
Parameter for:
Spatial index class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialIndex
Known implementations:
Parameter for:
Fixed maximum to use in sqrt scaling.
Parameter for:
Fixed minimum to use in sqrt scaling.
Parameter for:
Significance level to use for error function.
Default: 3.0
Parameter for:
Fixed mean to use in standard deviation scaling.
Parameter for:
Fixed minimum to use in sqrt scaling.
Parameter for:
Do not use the center as extra reference point.
Default: false
Parameter for:
Scale the reference points by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Parameter for:
Significance level to use for error function.
Default: 3.0
Parameter for:
Fixed mean to use in standard deviation scaling.
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.AbstractDimensionsSelectingDoubleDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.DimensionsSelectingEuclideanDistanceFunction
Known implementations:
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Parameter for:
Request output of performance time.
Default: false
Parameter for:
Number of digits to show (e.g. when visualizing outlier scores)
Default: 4
Parameter for:
Make the top k a binary scaling.
Default: false
Parameter for:
Number of outliers to keep.
Parameter for:
The size of the cache in bytes.
Default: 2147483647
Parameter for:
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
Parameter for:
The size of a page in bytes.
Default: 4000
Parameter for:
Enable verbose messages while performing the algorithm.
Default: false
Parameter for:
Maximum number of dimensions to display.
Default: 10
Parameter for:
Title to use for visualization window.
Parameter for:
Style properties file to use
Default: default
Parameter for: